Beyond probabilism

20 Jan, 2019 at 17:22 | Posted in Statistics & Econometrics | Comments Off on Beyond probabilism

“Getting philosophical” is not about articulating rarified concepts divorced from statistical practice. It is to provide tools to avoid obfuscating the terms and issues being bandied about …

mayoDo I hear a protest? “There is nothing philosophical about our criticism of statistical significance tests (someone might say). The problem is that a small P-value is invariably, and erroneously, interpreted as giving a small probability to the null hypothesis.” Really? P-values are not intended to be used this way; presupposing they ought to be so interpreted grows out of a specific conception of the role of probability in statistical inference. That conception is philosophical. Methods characterized through the lens of over-simple epistemological orthodoxies are methods misapplied and mischaracterized. This may lead one to lie, however unwittingly, about the nature and goals of statistical inference, when what we want is to tell what’s true about them …

One does not have evidence for a claim if nothing has been done to rule out ways the claim may be false. If data x agree with a claim C but the method used is practically guaranteed to find such agreement, and had little or no capability of finding flaws with C even if they exist, then we have bad evidence, no test …

Statistical inference uses data to reach claims about aspects of processes and mechanisms producing them, accompanied by an assessment of the properties of the inference methods: their capabilities to control and alert us to erroneous interpretations. We need to report if the method has satisfied the most minimal requirement for solving such a problem. Has anything been tested with a modicum of severity, or not? The severe tester also requires reporting of what has been poorly probed … Informal​ statistical testing, the crude dichotomy of “pass/fail” or “significant or not” will scarcely do. We must determine the magnitudes (and directions) of any statistical discrepancies warranted, and the limits to any substantive claims you may be entitled to infer from the statistical ones.

Deborah Mayo’s book underlines more than anything else the importance of not equating science with statistical calculation or applied probability theory.

The ‘frequentist’ long-run perspective in itself says nothing about how ‘severely’ tested are hypotheses and claims. It doesn’t give​ us the evidence we seek.

And ‘Bayesian’ consistency and coherence are as silent. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. Choosing between theories and hypotheses can never be a question of inner coherence and consistency.

Probabilism — in whatever form it takes — says absolutely​ nothing about reality.​

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